The present invention relates to hyperlinked document systems. More specifically, the invention relates to techniques for finding related hyperlinked documents using link-based analysis.
The Internet, and more specifically the World Wide Web, provides users all over the world with virtually unlimited amounts of information in the form of hyperlinked documents. As new information is added to the Web, more hyperlinked documents are added that include links to the existing web of information.
One of the reasons for the almost explosive growth of information on the Web is that virtually anyone can add hyperlinked documents, which will be immediately available to users around the world. For better or worse, the Web is virtually unstructured, meaning that users are free to add information to the Web in almost any way they desire. Although this provides great flexibility in adding information to the Web, it can significantly increase the difficulty in finding information that is desired.
Probably the most popular mechanism for finding information on the Web is to use word-based search engines. Word-based search engines allow a user to enter words, phrases, and other search criteria so that the search engine can retrieve the hyperlinked documents that best match the user's search criteria.
Word-based search engines have been tremendously successful in allowing users to find the information they desire on the Web. There are times, however, when a user wants to find hyperlinked documents that are related to and at the same level of generality to a selected hyperlinked document. For example, a user may be viewing a company's web site and wish to see other web sites for competitive companies. As another example, a user may have found a university's computer science department web site and the user may desire to see computer science department web sites of other universities. Traditional word-based search engines may not provide satisfactory results for these types of desired information.
Some web sites have recognized this deficiency and have taken on the pain staking process of categorizing the information on the Web. Although it is possible that the related hyperlinked documents that are desired are in a single category, it often happens that the related hyperlinked documents are spread throughout multiple categories. For example, if information regarding each university is placed in a separate category, one will not find a single category that includes information regarding the computer science departments of multiple universities. Additionally, categorizing the information on the Web takes a considerable amount of time and typically requires human decision making to categorize the information.
Therefore, what is needed are innovative techniques for finding related hyperlinked documents without requiring human categorization of the information.